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# PaddleOCR-VL NVIDIA Blackwell-Architecture GPUs Environment Configuration Tutorial
This tutorial provides guidance on configuring the environment for NVIDIA Blackwell-architecture GPUs. After completing the environment setup, please refer to the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md) to use PaddleOCR-VL.
NVIDIA Blackwell-architecture GPUs include, but are not limited to:
- RTX 5090
- RTX 5080
- RTX 5070、RTX 5070 Ti
- RTX 5060、RTX 5060 Ti
- RTX 5050
PaddleOCR-VL has been verified for accuracy and speed on the RTX 5070. However, due to hardware diversity, compatibility with other NVIDIA Blackwell-architecture GPUs has not yet been confirmed. We welcome the community to test on different hardware setups and share your results.
Before starting the tutorial, **please ensure that your NVIDIA driver supports CUDA 12.9 or higher**.
## 1. Environment Preparation
This section introduces how to set up the PaddleOCR-VL runtime environment using one of the following two methods:
- Method 1: Use the official Docker image.
- Method 2: Manually install PaddlePaddle and PaddleOCR.
**We strongly recommend using the Docker image to minimize potential environment-related issues.**
### 1.1 Method 1: Using Docker Image
We recommend using the official Docker image (requires Docker version >= 19.03, GPU-equipped machine with NVIDIA driver supporting CUDA 12.9 or higher):
```shell
docker run \
-it \
--gpus all \
--network host \
--user root \
ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:latest-nvidia-gpu-sm120 \
/bin/bash
# Call PaddleOCR CLI or Python API in the container
```
If you wish to use PaddleOCR-VL in an offline environment, replace `ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:latest-nvidia-gpu-sm120` (image size approximately 10 GB) in the above command with the offline version image `ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:latest-nvidia-gpu-sm120-offline` (image size approximately 12 GB).
> TIP:
> Images with the `latest-xxx` tag correspond to the latest version of PaddleOCR. If you want to use a specific version of the PaddleOCR image, you can replace `latest` in the tag with the desired version number: `paddleocr<major>.<minor>`.
> For example:
> `ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:paddleocr3.3-nvidia-gpu-sm120-offline`
### 1.2 Method 2: Manually Install PaddlePaddle and PaddleOCR
If Docker is not an option, you can manually install PaddlePaddle and PaddleOCR. Python version 3.8–3.12 is required.
**We strongly recommend installing PaddleOCR-VL in a virtual environment to avoid dependency conflicts.** For example, create a virtual environment using Python's standard venv library:
```shell
# Create a virtual environment
python -m venv .venv_paddleocr
# Activate the environment
source .venv_paddleocr/bin/activate
```
Run the following commands to complete the installation:
```shell
# Note that PaddlePaddle for cu129 is being installed here
python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu129/
python -m pip install -U "paddleocr[doc-parser]"
```
> **Please ensure that PaddlePaddle framework version 3.2.1 or higher is installed.**
## 2. Quick Start
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
## 3. Improving VLM Inference Performance Using Inference Acceleration Frameworks
The inference performance under default configurations may not be fully optimized and may not meet actual production requirements. This section introduces how to use the vLLM and SGLang inference acceleration frameworks to enhance PaddleOCR-VL's inference performance.
### 3.1 Starting the VLM Inference Service
There are two methods to start the VLM inference service; choose one:
- Method 1: Start the service using the official Docker image.
- Method 2: Manually install dependencies and start the service via PaddleOCR CLI.
**We strongly recommend using the Docker image to minimize potential environment-related issues.**
#### 3.1.1 Method 1: Using Docker Image
PaddleOCR provides a Docker image for quickly starting the vLLM inference service. Use the following command to start the service (requires Docker version >= 19.03, GPU-equipped machine with NVIDIA driver supporting CUDA 12.9 or higher):
```shell
docker run \
-it \
--gpus all \
--network host \
ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu-sm120 \
paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --host 0.0.0.0 --port 8118 --backend vllm
```
If you wish to start the service in an offline environment, replace `ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu-sm120` (image size approximately 13 GB) in the above command with the offline version image `ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu-sm120-offline` (image size approximately 15 GB).
When launching the vLLM inference service, we provide a set of default parameter settings. If you need to adjust parameters such as GPU memory usage, you can configure additional parameters yourself. Please refer to [3.3.1 Server-side Parameter Adjustment](./PaddleOCR-VL.en.md#331-server-side-parameter-adjustment) to create a configuration file, then mount the file into the container and specify the configuration file using `backend_config` in the command to start the service, for example:
```shell
docker run \
-it \
--rm \
--gpus all \
--network host \
-v vllm_config.yml:/tmp/vllm_config.yml \
ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu-sm120 \
paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --host 0.0.0.0 --port 8118 --backend vllm --backend_config /tmp/vllm_config.yml
```
> TIP:
> Images with the `latest-xxx` tag correspond to the latest version of PaddleOCR. If you want to use a specific version of the PaddleOCR image, you can replace `latest` in the tag with the desired version number: `paddleocr<major>.<minor>`.
> For example:
> `ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:paddleocr3.3-nvidia-gpu-sm120-offline`
#### 3.1.2 Method 2: Installation and Usage via PaddleOCR CLI
Due to potential dependency conflicts between inference acceleration frameworks and PaddlePaddle, it is recommended to install them in a virtual environment:
```shell
# If a virtual environment is currently activated, deactivate it first using `deactivate`
# Create a virtual environment
python -m venv .venv_vlm
# Activate the environment
source .venv_vlm/bin/activate
```
vLLM and SGLang depend on FlashAttention, and installing FlashAttention may require CUDA compilation tools such as `nvcc`. If these tools are not available in your environment (for example, when using the `paddleocr-vl` image), you can obtain a prebuilt FlashAttention package (version 2.8.3 required) from [this repository](https://github.com/mjun0812/flash-attention-prebuild-wheels), install it first, and then proceed with subsequent commands. For example, in the `paddleocr-vl` image, run `python -m pip install https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.3.14/flash_attn-2.8.3+cu128torch2.8-cp310-cp310-linux_x86_64.whl`. This step is not required for FastDeploy.
Install PaddleOCR and the dependencies of inference acceleration services, using vLLM as an example:
```shell
# Install PaddleOCR
python -m pip install "paddleocr[doc-parser]"
# Install inference acceleration service dependencies
paddleocr install_genai_server_deps vllm
```
Usage of the `paddleocr install_genai_server_deps` command:
```shell
paddleocr install_genai_server_deps <inference acceleration framework name>
```
Currently supported framework names are `vllm` and `sglang`, corresponding to vLLM and SGLang, respectively.
After installation, you can start the service using the `paddleocr genai_server` command:
```shell
paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --backend vllm --port 8118
```
The parameters supported by this command are as follows:
| Parameter | Description |
|------------------|-----------------------------------------------------------------------------|
| `--model_name` | Name of the model |
| `--model_dir` | Directory containing the model |
| `--host` | Server hostname |
| `--port` | Server port number |
| `--backend` | Backend name, i.e., the name of the inference acceleration framework being used; options are `vllm` or `sglang` |
| `--backend_config`| YAML file specifying backend configuration |
### 3.2 Client Usage
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
### 3.3 Performance Tuning
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
## 4. Service Deployment
This section mainly introduces how to deploy PaddleOCR-VL as a service and invoke it. There are two methods available; choose one:
- Method 1: Deploy using Docker Compose.
- Method 2: Manually install dependencies for deployment.
>Please note that the PaddleOCR-VL service introduced in this section differs from the VLM inference service in the previous section: the latter is responsible for only one part of the complete process (i.e., VLM inference) and is called as an underlying service by the former.
### 4.1 Method 1: Deploy Using Docker Compose
1. Download the Compose file and the environment variable configuration file separately from [here](https://github.com/PaddlePaddle/PaddleOCR/blob/main/deploy/paddleocr_vl_docker/accelerators/nvidia-gpu-sm120/compose.yaml) and [here](https://github.com/PaddlePaddle/PaddleOCR/blob/main/deploy/paddleocr_vl_docker/accelerators/nvidia-gpu-sm120/.env) to your local machine.
2. Execute the following command in the directory containing the `compose.yaml` and `.env` files to start the server, which will listen on port **8080** by default:
```shell
# Must be executed in the directory containing compose.yaml and .env files
docker compose up
```
After startup, you will see output similar to the following:
```text
paddleocr-vl-api | INFO: Started server process [1]
paddleocr-vl-api | INFO: Waiting for application startup.
paddleocr-vl-api | INFO: Application startup complete.
paddleocr-vl-api | INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
```
This method accelerates VLM inference using the vLLM framework and is more suitable for production environment deployment.
Additionally, after starting the server in this manner, no internet connection is required except for image pulling. For deployment in an offline environment, you can first pull the images involved in the Compose file on a connected machine, export them, and transfer them to the offline machine for import to start the service in an offline environment.
Docker Compose starts two containers sequentially by reading configurations from the `.env` and `compose.yaml` files, running the underlying VLM inference service and the PaddleOCR-VL service (pipeline service) respectively.
The meanings of each environment variable contained in the `.env` file are as follows:
```
- `API_IMAGE_TAG_SUFFIX`: The tag suffix of the image used to launch the pipeline service.
- `VLM_BACKEND`: The VLM inference backend.
- `VLM_IMAGE_TAG_SUFFIX`: The tag suffix of the image used to launch the VLM inference service.
```
You can modify `compose.yaml` to meet custom requirements, for example:
<details>
<summary>1. Change the port of the PaddleOCR-VL service</summary>
Edit <code>paddleocr-vl-api.ports</code> in the <code>compose.yaml</code> file to change the port. For example, if you need to change the service port to 8111, make the following modifications:
```diff
paddleocr-vl-api:
...
ports:
- - 8080:8080
+ - 8111:8080
...
```
</details>
<details>
<summary>2. Specify the GPU used by the PaddleOCR-VL service</summary>
Edit <code>environment</code> in the <code>compose.yaml</code> file to change the GPU used. For example, if you need to use card 1 for deployment, make the following modifications:
```diff
paddleocr-vl-api:
...
deploy:
resources:
reservations:
devices:
- driver: nvidia
- device_ids: ["0"]
+ device_ids: ["1"]
capabilities: [gpu]
...
paddleocr-vlm-server:
...
deploy:
resources:
reservations:
devices:
- driver: nvidia
- device_ids: ["0"]
+ device_ids: ["1"]
capabilities: [gpu]
...
```
</details>
<details>
<summary>3. Adjust VLM server-side configuration</summary>
If you want to adjust the VLM server configuration, refer to <a href="./PaddleOCR-VL.en.md#331-server-parameter-adjustment">3.3.1 Server Parameter Adjustment</a> to generate a configuration file.
After generating the configuration file, add the following <code>paddleocr-vlm-server.volumes</code> and <code>paddleocr-vlm-server.command</code> fields to your <code>compose.yaml</code>. Replace <code>/path/to/your_config.yaml</code> with your actual configuration file path.
```yaml
paddleocr-vlm-server:
...
volumes: /path/to/your_config.yaml:/home/paddleocr/vlm_server_config.yaml
command: paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --host 0.0.0.0 --port 8118 --backend vllm --backend_config /home/paddleocr/vlm_server_config.yaml
...
```
</details>
<details>
<summary>4. Adjust pipeline-related configurations (such as model path, batch size, deployment device, etc.)</summary>
Refer to the <a href="./PaddleOCR-VL.en.md#44-pipeline-configuration-adjustment-instructions">4.4 Pipeline Configuration Adjustment Instructions</a> section.
</details>
### 4.2 Method 2: Manually Deployment
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
### 4.3 Client Invocation Methods
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
### 4.4 Pipeline Configuration Adjustment Instructions
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
## 5. Model Fine-Tuning
Please refer to the corresponding section in the [PaddleOCR-VL Usage Tutorial](./PaddleOCR-VL.en.md).
